Heat flow, depth–temperature variations and stored thermal energy for enhanced geothermal systems in Canada
Bibliographic record
Abstract
In order to help assessment of enhanced geothermal energy potential in Canada, we constructed a new series of heatflow and depth–temperature distribution maps (down to 10 km). We focus on high-temperature resources (>150 °C) capable of electrical production. Maps presented show large temperature variability, related mainly to heat flow patterns. The highest temperatures occur in western and northern Canada. Here temperatures greater than 150 °C, required for enhanced geothermal systems (EGS), can be reached at reasonable drilling depths of <5 km. Heat flow, by itself however, is not a sufficient tool to predict areas of high energy content. A combination of thick low thermal conductivity sedimentary blankets and moderate to high heat flow areas can generate targets that are as favorable as regions with high conductivity and high heat flow. Some moderate heat flow areas in the deeper parts of the Western Canada Sedimentary Basin have heat content comparable to high heat flow zones of the the Canadian Cordillera. The magnitude of in-place thermal energy available for future heat ‘mining/farming’ was esitmated throughout Canada by calculating heat released through cooling a defined rock volume through a fixed temperature change. These estimates show the first-order appoximation of available geothermal heat content. The fraction of true heat energy available will be as low as 0.02 of these values. However, even this more limited energy production could be large enough to be a considerable future renewable energy resource for Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".